




"""simple docstring"""





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from ..image_utils import load_image


if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

A				=							logging.get_logger(__name__)


A				=							Dict[str, Any]
A				=							List[Prediction]



@add_end_docstrings(__magic_name__ )
class   a__					(						__magic_name__ ):


			def __init__(   self   :     Optional[int]   ,	*UpperCamelCase_   :     Optional[int]   ,	**UpperCamelCase_   :     Any):


						"""simple docstring"""




						super().__init__(*UpperCamelCase_   ,	**UpperCamelCase_)

						if self.framework == "tf":
									raise ValueError(F"The {self.__class__} is only available in PyTorch.")

						requires_backends(self   ,	"vision")
						self.check_model_type(
						    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))


			def    a_  (   self   :     Dict   ,	**UpperCamelCase_   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       {}
						if "threshold" in kwargs:
									__UpperCAmelCase						:			Dict												=       kwargs["threshold"]
						return {}, {}, postprocess_kwargs


			def __call__(   self   :     str   ,	*UpperCamelCase_   :     Optional[int]   ,	**UpperCamelCase_   :     Tuple):


						"""simple docstring"""




						return super().__call__(*UpperCamelCase_   ,	**UpperCamelCase_)


			def    a_  (   self   :     int   ,	UpperCamelCase_   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       load_image(UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       torch.IntTensor([[image.height, image.width]])
						__UpperCAmelCase						:			str												=       self.image_processor(images=[image]   ,	return_tensors="pt")
						if self.tokenizer is not None:
									__UpperCAmelCase						:			Any												=       self.tokenizer(text=inputs["words"]   ,	boxes=inputs["boxes"]   ,	return_tensors="pt")
						__UpperCAmelCase						:			List[Any]												=       target_size
						return inputs


			def    a_  (   self   :     Tuple   ,	UpperCamelCase_   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       model_inputs.pop("target_size")
						__UpperCAmelCase						:			Tuple												=       self.model(**UpperCamelCase_)
						__UpperCAmelCase						:			int												=       outputs.__class__({"target_size": target_size, **outputs})
						if self.tokenizer is not None:
									__UpperCAmelCase						:			str												=       model_inputs["bbox"]
						return model_outputs


			def    a_  (   self   :     int   ,	UpperCamelCase_   :     Tuple   ,	UpperCamelCase_   :     Tuple=0.9):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       model_outputs["target_size"]
						if self.tokenizer is not None:
									# This is a LayoutLMForTokenClassification variant.
									# The OCR got the boxes and the model classified the words.
									__UpperCAmelCase						,  __UpperCAmelCase						:			str												=       target_size[0].tolist()

									def unnormalize(UpperCamelCase_   :     List[str]):
												return self._get_bounding_box(
												    torch.Tensor(
												        [
												            (width * bbox[0] / 1000),
												            (height * bbox[1] / 1000),
												            (width * bbox[2] / 1000),
												            (height * bbox[3] / 1000),
												        ]))

									__UpperCAmelCase						,  __UpperCAmelCase						:			int												=       model_outputs["logits"].squeeze(0).softmax(dim=-1).max(dim=-1)
									__UpperCAmelCase						:			List[str]												=       [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
									__UpperCAmelCase						:			Optional[Any]												=       [unnormalize(UpperCamelCase_) for bbox in model_outputs["bbox"].squeeze(0)]
									__UpperCAmelCase						:			Union[str, Any]												=       ["score", "label", "box"]
									__UpperCAmelCase						:			Dict												=       [dict(zip(UpperCamelCase_   ,	UpperCamelCase_)) for vals in zip(scores.tolist()   ,	UpperCamelCase_   ,	UpperCamelCase_) if vals[0] > threshold]
						else:
									# This is a regular ForObjectDetectionModel
									__UpperCAmelCase						:			List[str]												=       self.image_processor.post_process_object_detection(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			Optional[int]												=       raw_annotations[0]
									__UpperCAmelCase						:			Optional[int]												=       raw_annotation["scores"]
									__UpperCAmelCase						:			Dict												=       raw_annotation["labels"]
									__UpperCAmelCase						:			Tuple												=       raw_annotation["boxes"]

									__UpperCAmelCase						:			List[Any]												=       scores.tolist()
									__UpperCAmelCase						:			Any												=       [self.model.config.idalabel[label.item()] for label in labels]
									__UpperCAmelCase						:			Tuple												=       [self._get_bounding_box(UpperCamelCase_) for box in boxes]

									# {"scores": [...], ...} --> [{"score":x, ...}, ...]
									__UpperCAmelCase						:			Union[str, Any]												=       ["score", "label", "box"]
									__UpperCAmelCase						:			Optional[int]												=       [
									    dict(zip(UpperCamelCase_   ,	UpperCamelCase_))
									    for vals in zip(raw_annotation["scores"]   ,	raw_annotation["labels"]   ,	raw_annotation["boxes"])
									]

						return annotation


			def    a_  (   self   :     int   ,	UpperCamelCase_   :     "torch.Tensor"):


						"""simple docstring"""




						if self.framework != "pt":
									raise ValueError("The ObjectDetectionPipeline is only available in PyTorch.")
						__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			List[str]												=       box.int().tolist()
						__UpperCAmelCase						:			Dict												=       {
						    "xmin": xmin,
						    "ymin": ymin,
						    "xmax": xmax,
						    "ymax": ymax,
						}
						return bbox





lowercase			:				Dict   =     [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					0
							while number:
														# Increased Speed Slightly by checking every 5 digits together.
														sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
														number //= 100000

							return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowercase			:				list[bool | None]   =     [None] * 10_00_00_00
lowercase			:				int   =     True
lowercase			:				Tuple   =     False






def        snake_case__		(    lowerCamelCase_		):

							if CHAINS[number - 1] is not None:
														return CHAINS[number - 1]  # type: ignore

							A      :					int      		=					chain(next_number(lowerCamelCase_		)		)
							A      :					Dict      		=					number_chain

							while number < 10000000:
														A      :					Any      		=					number_chain
														number *= 10

							return number_chain






def        snake_case__		(    lowerCamelCase_ = 10000000		):
							for i in range(1     ,				lowerCamelCase_		):
														if CHAINS[i] is None:
																					chain(i + 1		)

							return CHAINS[:number].count(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"{solution() = }")






